MueLu_SaPFactory_def.hpp

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#ifndef MUELU_SAPFACTORY_DEF_HPP
#define MUELU_SAPFACTORY_DEF_HPP

#include <Xpetra_Matrix.hpp>
#include <Xpetra_IteratorOps.hpp> // containing routines to generate Jacobi iterator
#include <Xpetra_IO.hpp>
#include <sstream>

#include "MueLu_SaPFactory_decl.hpp"

#include "MueLu_FactoryManagerBase.hpp"
#include "MueLu_Level.hpp"
#include "MueLu_MasterList.hpp"
#include "MueLu_Monitor.hpp"
#include "MueLu_PerfUtils.hpp"
#include "MueLu_SingleLevelFactoryBase.hpp"
#include "MueLu_TentativePFactory.hpp"
#include "MueLu_Utilities.hpp"

namespace MueLu {

  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  RCP<const ParameterList> SaPFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::GetValidParameterList() const {
    RCP<ParameterList> validParamList = rcp(new ParameterList());

#define SET_VALID_ENTRY(name) validParamList->setEntry(name, MasterList::getEntry(name))
    SET_VALID_ENTRY("sa: damping factor");
    SET_VALID_ENTRY("sa: calculate eigenvalue estimate");
    SET_VALID_ENTRY("sa: eigenvalue estimate num iterations");
    SET_VALID_ENTRY("sa: use rowsumabs diagonal scaling");
    SET_VALID_ENTRY("sa: enforce constraints");
    SET_VALID_ENTRY("tentative: calculate qr");
    SET_VALID_ENTRY("sa: max eigenvalue");
    SET_VALID_ENTRY("sa: rowsumabs diagonal replacement tolerance");
    SET_VALID_ENTRY("sa: rowsumabs diagonal replacement value");
    SET_VALID_ENTRY("sa: rowsumabs replace single entry row with zero");
    SET_VALID_ENTRY("sa: rowsumabs use automatic diagonal tolerance");
#undef  SET_VALID_ENTRY

    validParamList->set< RCP<const FactoryBase> >("A",              Teuchos::null, "Generating factory of the matrix A used during the prolongator smoothing process");
    validParamList->set< RCP<const FactoryBase> >("P",              Teuchos::null, "Tentative prolongator factory");

    // Make sure we don't recursively validate options for the matrixmatrix kernels
    ParameterList norecurse;
    norecurse.disableRecursiveValidation();
    validParamList->set<ParameterList> ("matrixmatrix: kernel params", norecurse, "MatrixMatrix kernel parameters");


    return validParamList;
  }

  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  void SaPFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::DeclareInput(Level &fineLevel, Level &coarseLevel) const {
    Input(fineLevel, "A");

    // Get default tentative prolongator factory
    // Getting it that way ensure that the same factory instance will be used for both SaPFactory and NullspaceFactory.
    RCP<const FactoryBase> initialPFact = GetFactory("P");
    if (initialPFact == Teuchos::null) { initialPFact = coarseLevel.GetFactoryManager()->GetFactory("Ptent"); }
    coarseLevel.DeclareInput("P", initialPFact.get(), this); // --
  }

  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  void SaPFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::Build(Level& fineLevel, Level &coarseLevel) const {
    return BuildP(fineLevel, coarseLevel);
  }

  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  void SaPFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::BuildP(Level &fineLevel, Level &coarseLevel) const {
    FactoryMonitor m(*this, "Prolongator smoothing", coarseLevel);

    std::string levelIDs = toString(coarseLevel.GetLevelID());

    const std::string prefix = "MueLu::SaPFactory(" + levelIDs + "): ";

    typedef typename Teuchos::ScalarTraits<SC>::coordinateType Coordinate;
    typedef typename Teuchos::ScalarTraits<SC>::magnitudeType MT;


    // Get default tentative prolongator factory
    // Getting it that way ensure that the same factory instance will be used for both SaPFactory and NullspaceFactory.
    // -- Warning: Do not use directly initialPFact_. Use initialPFact instead everywhere!
    RCP<const FactoryBase> initialPFact = GetFactory("P");
    if (initialPFact == Teuchos::null) { initialPFact = coarseLevel.GetFactoryManager()->GetFactory("Ptent"); }
    const ParameterList& pL = GetParameterList();

    // Level Get
    RCP<Matrix> A     = Get< RCP<Matrix> >(fineLevel, "A");
    RCP<Matrix> Ptent = coarseLevel.Get< RCP<Matrix> >("P", initialPFact.get());
    RCP<Matrix> finalP;
    // If Tentative facctory bailed out (e.g., number of global aggregates is 0), then SaPFactory bails
   //  This level will ultimately be removed in MueLu_Hierarchy_defs.h via a resize()
    if (Ptent == Teuchos::null) {
      finalP = Teuchos::null;
      Set(coarseLevel, "P", finalP);
      return;
    }

    if (restrictionMode_) {
      SubFactoryMonitor m2(*this, "Transpose A", coarseLevel);
      A = Utilities::Transpose(*A, true); // build transpose of A explicitely
    }

    // Build final prolongator

    // Reuse pattern if available
    RCP<ParameterList> APparams = rcp(new ParameterList);;
    if(pL.isSublist("matrixmatrix: kernel params"))
      APparams->sublist("matrixmatrix: kernel params") = pL.sublist("matrixmatrix: kernel params");

    if (coarseLevel.IsAvailable("AP reuse data", this)) {
      GetOStream(static_cast<MsgType>(Runtime0 | Test)) << "Reusing previous AP data" << std::endl;

      APparams = coarseLevel.Get< RCP<ParameterList> >("AP reuse data", this);

      if (APparams->isParameter("graph"))
        finalP = APparams->get< RCP<Matrix> >("graph");
    }
    // By default, we don't need global constants for SaP
    APparams->set("compute global constants: temporaries",APparams->get("compute global constants: temporaries",false));
    APparams->set("compute global constants",APparams->get("compute global constants",false));

    const SC dampingFactor      = as<SC>(pL.get<double>("sa: damping factor"));
    const LO maxEigenIterations = as<LO>(pL.get<int>   ("sa: eigenvalue estimate num iterations"));
    const bool estimateMaxEigen =        pL.get<bool>  ("sa: calculate eigenvalue estimate");
    const bool useAbsValueRowSum=        pL.get<bool>  ("sa: use rowsumabs diagonal scaling");
    const bool doQRStep         =        pL.get<bool>("tentative: calculate qr");
    const bool enforceConstraints=       pL.get<bool>("sa: enforce constraints");
    const MT   userDefinedMaxEigen =  as<MT>(pL.get<double>("sa: max eigenvalue"));
    typedef typename Teuchos::ScalarTraits<Scalar>::magnitudeType Magnitude;
    double dTol = pL.get<double>("sa: rowsumabs diagonal replacement tolerance");
    const Magnitude diagonalReplacementTolerance = (dTol == as<double>(-1) ? Teuchos::ScalarTraits<Scalar>::eps()*100 : as<Magnitude>(pL.get<double>("sa: rowsumabs diagonal replacement tolerance")));
    const SC diagonalReplacementValue =  as<SC>(pL.get<double>("sa: rowsumabs diagonal replacement value"));
    const bool replaceSingleEntryRowWithZero = pL.get<bool>("sa: rowsumabs replace single entry row with zero");
    const bool useAutomaticDiagTol =       pL.get<bool>("sa: rowsumabs use automatic diagonal tolerance");

    // Sanity checking
    TEUCHOS_TEST_FOR_EXCEPTION(doQRStep && enforceConstraints,Exceptions::RuntimeError,
                               "MueLu::TentativePFactory::MakeTentative: cannot use 'enforce constraints' and 'calculate qr' at the same time");

    if (dampingFactor != Teuchos::ScalarTraits<SC>::zero()) {

      Scalar lambdaMax;
      Teuchos::RCP<Vector> invDiag;
      if (userDefinedMaxEigen == -1.)
      {
        SubFactoryMonitor m2(*this, "Eigenvalue estimate", coarseLevel);
        lambdaMax = A->GetMaxEigenvalueEstimate();
        if (lambdaMax == -Teuchos::ScalarTraits<SC>::one() || estimateMaxEigen) {
          GetOStream(Statistics1) << "Calculating max eigenvalue estimate now (max iters = "<< maxEigenIterations <<
          ( (useAbsValueRowSum) ?  ", use rowSumAbs diagonal)" :  ", use point diagonal)") << std::endl;
          Coordinate stopTol = 1e-4;
          if (useAbsValueRowSum) {
            const bool returnReciprocal=true;
            invDiag = Utilities::GetLumpedMatrixDiagonal(*A,returnReciprocal,
                                                        diagonalReplacementTolerance,
                                                        diagonalReplacementValue,
                                                        replaceSingleEntryRowWithZero,
                                                        useAutomaticDiagTol);
            TEUCHOS_TEST_FOR_EXCEPTION(invDiag.is_null(), Exceptions::RuntimeError,
                                       "SaPFactory: eigenvalue estimate: diagonal reciprocal is null.");
            lambdaMax = Utilities::PowerMethod(*A, invDiag, maxEigenIterations, stopTol);
          } else
            lambdaMax = Utilities::PowerMethod(*A, true, maxEigenIterations, stopTol);
          A->SetMaxEigenvalueEstimate(lambdaMax);
        } else {
          GetOStream(Statistics1) << "Using cached max eigenvalue estimate" << std::endl;
        }
      }
      else {
          lambdaMax = userDefinedMaxEigen;
          A->SetMaxEigenvalueEstimate(lambdaMax);
      }
      GetOStream(Statistics1) << "Prolongator damping factor = " << dampingFactor/lambdaMax << " (" << dampingFactor << " / " << lambdaMax << ")" << std::endl;

      {
        SubFactoryMonitor m2(*this, "Fused (I-omega*D^{-1} A)*Ptent", coarseLevel);
        if (!useAbsValueRowSum)
          invDiag = Utilities::GetMatrixDiagonalInverse(*A); //default
        else if (invDiag == Teuchos::null) {
          GetOStream(Runtime0) << "Using rowsumabs diagonal" << std::endl;
          const bool returnReciprocal=true;
          invDiag = Utilities::GetLumpedMatrixDiagonal(*A,returnReciprocal,
                                                        diagonalReplacementTolerance,
                                                        diagonalReplacementValue,
                                                        replaceSingleEntryRowWithZero,
                                                        useAutomaticDiagTol);
          TEUCHOS_TEST_FOR_EXCEPTION(invDiag.is_null(), Exceptions::RuntimeError, "SaPFactory: diagonal reciprocal is null.");
        } 
  
        SC omega = dampingFactor / lambdaMax;
        TEUCHOS_TEST_FOR_EXCEPTION(!std::isfinite(Teuchos::ScalarTraits<SC>::magnitude(omega)), Exceptions::RuntimeError, "Prolongator damping factor needs to be finite.");

        // finalP = Ptent + (I - \omega D^{-1}A) Ptent
        finalP = Xpetra::IteratorOps<Scalar,LocalOrdinal,GlobalOrdinal,Node>::Jacobi(omega, *invDiag, *A, *Ptent, finalP,
                    GetOStream(Statistics2), std::string("MueLu::SaP-")+levelIDs, APparams);
        if (enforceConstraints) {
           if (A->GetFixedBlockSize() == 1) optimalSatisfyPConstraintsForScalarPDEs( finalP);
           else                             SatisfyPConstraints( A, finalP);
        }
      }

    } else {
      finalP = Ptent;
    }

    // Level Set
    if (!restrictionMode_) {
      // The factory is in prolongation mode
      if(!finalP.is_null()) {std::ostringstream oss; oss << "P_" << coarseLevel.GetLevelID(); finalP->setObjectLabel(oss.str());}
      Set(coarseLevel, "P",             finalP);
    
      APparams->set("graph", finalP);
      Set(coarseLevel, "AP reuse data", APparams);

      // NOTE: EXPERIMENTAL
      if (Ptent->IsView("stridedMaps"))
        finalP->CreateView("stridedMaps", Ptent);

      if (IsPrint(Statistics2)) {
        RCP<ParameterList> params = rcp(new ParameterList());
        params->set("printLoadBalancingInfo", true);
        params->set("printCommInfo",          true);
        GetOStream(Statistics2) << PerfUtils::PrintMatrixInfo(*finalP, "P", params);
      }

    } else {
      // The factory is in restriction mode
      RCP<Matrix> R;
      {
        SubFactoryMonitor m2(*this, "Transpose P", coarseLevel);
        R = Utilities::Transpose(*finalP, true);
        if(!R.is_null()) {std::ostringstream oss; oss << "R_" << coarseLevel.GetLevelID(); R->setObjectLabel(oss.str());}
      }

      Set(coarseLevel, "R", R);

      // NOTE: EXPERIMENTAL
      if (Ptent->IsView("stridedMaps"))
        R->CreateView("stridedMaps", Ptent, true/*transposeA*/);

      if (IsPrint(Statistics2)) {
        RCP<ParameterList> params = rcp(new ParameterList());
        params->set("printLoadBalancingInfo", true);
        params->set("printCommInfo",          true);
        GetOStream(Statistics2) << PerfUtils::PrintMatrixInfo(*R, "R", params);
      }
    }

  } //Build()

  // Analyze the grid transfer produced by smoothed aggregation and make
  // modifications if it does not look right. In particular, if there are
  // negative entries or entries larger than 1, modify P's rows. 
  //
  // Note: this kind of evaluation probably only makes sense if not doing QR
  // when constructing tentative P.
  //
  // For entries that do not satisfy the two constraints (>= 0  or <=1) we set
  // these entries to the constraint value and modify the rest of the row
  // so that the row sum remains the same as before by adding an equal
  // amount to each remaining entry. However, if the original row sum value
  // violates the constraints, we set the row sum back to 1 (the row sum of 
  // tentative P). After doing the modification to a row, we need to check 
  // again the entire row to make sure that the modified row does not violate
  // the constraints.

  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  void SaPFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::SatisfyPConstraints(const RCP<Matrix> A, RCP<Matrix>& P) const {

    const Scalar zero = Teuchos::ScalarTraits<Scalar>::zero();
    const Scalar one  = Teuchos::ScalarTraits<Scalar>::one();
    LO nPDEs = A->GetFixedBlockSize();
    Teuchos::ArrayRCP<Scalar> ConstraintViolationSum(nPDEs);
    Teuchos::ArrayRCP<Scalar> Rsum(nPDEs);
    Teuchos::ArrayRCP<size_t> nPositive(nPDEs);
    for (size_t k=0; k < (size_t) nPDEs; k++) ConstraintViolationSum[k] = zero;
    for (size_t k=0; k < (size_t) nPDEs; k++) Rsum[k] = zero;
    for (size_t k=0; k < (size_t) nPDEs; k++) nPositive[k] = 0;


    for (size_t i = 0; i < as<size_t>(P->getRowMap()->getLocalNumElements()); i++) {

      Teuchos::ArrayView<const LocalOrdinal> indices;
      Teuchos::ArrayView<const Scalar> vals1;
      Teuchos::ArrayView<      Scalar> vals;
      P->getLocalRowView((LO) i, indices, vals1);
      size_t nnz = indices.size();
      if (nnz == 0) continue;

      vals = ArrayView<Scalar>(const_cast<SC*>(vals1.getRawPtr()), nnz);


      bool checkRow = true;

      while (checkRow) { 

        // check constraints and compute the row sum
    
        for (LO j = 0; j < indices.size(); j++)  {
          Rsum[ j%nPDEs ] += vals[j]; 
          if (Teuchos::ScalarTraits<SC>::real(vals[j]) < Teuchos::ScalarTraits<SC>::real(zero)) { 
            ConstraintViolationSum[ j%nPDEs ] += vals[j]; 
            vals[j] = zero;
          }
          else {
            if (Teuchos::ScalarTraits<SC>::real(vals[j]) != Teuchos::ScalarTraits<SC>::real(zero))
              (nPositive[ j%nPDEs])++;
    
            if (Teuchos::ScalarTraits<SC>::real(vals[j]) > Teuchos::ScalarTraits<SC>::real(1.00001  )) { 
              ConstraintViolationSum[ j%nPDEs ] += (vals[j] - one); 
              vals[j] = one;
            }
          }
        }
    
        checkRow = false;

        // take into account any row sum that violates the contraints
    
        for (size_t k=0; k < (size_t) nPDEs; k++) {

          if (Teuchos::ScalarTraits<SC>::real(Rsum[ k ]) < Teuchos::ScalarTraits<SC>::magnitude(zero)) {
              ConstraintViolationSum[k] +=  (-Rsum[k]);  // rstumin 
          }
          else if (Teuchos::ScalarTraits<SC>::real(Rsum[ k ]) > Teuchos::ScalarTraits<SC>::magnitude(1.00001)) {
              ConstraintViolationSum[k] += (one - Rsum[k]);  // rstumin
          }
        }

        // check if row need modification 
        for (size_t k=0; k < (size_t) nPDEs; k++) {
          if (Teuchos::ScalarTraits<SC>::magnitude(ConstraintViolationSum[ k ]) != Teuchos::ScalarTraits<SC>::magnitude(zero))
             checkRow = true;
        }
        // modify row
        if (checkRow) {
           for (LO j = 0; j < indices.size(); j++)  {
             if (Teuchos::ScalarTraits<SC>::real(vals[j]) > Teuchos::ScalarTraits<SC>::real(zero)) { 
                vals[j] += (ConstraintViolationSum[j%nPDEs]/ (as<Scalar>(nPositive[j%nPDEs])));
             }
           }
           for (size_t k=0; k < (size_t) nPDEs; k++) ConstraintViolationSum[k] = zero; 
        }
        for (size_t k=0; k < (size_t) nPDEs; k++) Rsum[k] = zero; 
        for (size_t k=0; k < (size_t) nPDEs; k++) nPositive[k] = 0;
      } // while (checkRow) ...
    } // for (size_t i = 0; i < as<size_t>(P->getRowMap()->getNumNodeElements()); i++) ...
  } //SatsifyPConstraints()

  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  void SaPFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::optimalSatisfyPConstraintsForScalarPDEs(RCP<Matrix>& P) const {

    const Scalar zero = Teuchos::ScalarTraits<Scalar>::zero();
    const Scalar one  = Teuchos::ScalarTraits<Scalar>::one();

    LocalOrdinal  maxEntriesPerRow = 100; // increased later if needed 
    Teuchos::ArrayRCP<Scalar> scalarData(3*maxEntriesPerRow);
    bool hasFeasible;

    for (size_t i = 0; i < as<size_t>(P->getRowMap()->getLocalNumElements()); i++) {

      Teuchos::ArrayView<const LocalOrdinal> indices;
      Teuchos::ArrayView<const Scalar> vals1;
      Teuchos::ArrayView<      Scalar> vals;
      P->getLocalRowView((LocalOrdinal) i, indices, vals1);
      size_t nnz = indices.size();
      if (nnz != 0) {

        vals = ArrayView<Scalar>(const_cast<SC*>(vals1.getRawPtr()), nnz);
        Scalar rsumTarget = zero;
        for (size_t j = 0; j < nnz; j++) rsumTarget += vals[j];

        if (nnz > as<size_t>(maxEntriesPerRow)) {
          maxEntriesPerRow = nnz*3;
          scalarData.resize(3*maxEntriesPerRow);
        }
        hasFeasible = constrainRow(vals.getRawPtr(), as<LocalOrdinal>(nnz), zero , one, rsumTarget, vals.getRawPtr(), scalarData.getRawPtr());

        if (!hasFeasible) { // just set all entries to the same value giving a row sum of 1
          for (size_t j = 0; j < nnz; j++) vals[j] = one/as<Scalar>(nnz);
        }
      }

    } // for (size_t i = 0; i < as<size_t>(P->getRowMap()->getNumNodeElements()); i++) ...
  } //SatsifyPConstraints()

  template <class Scalar, class LocalOrdinal, class GlobalOrdinal, class Node>
  bool SaPFactory<Scalar, LocalOrdinal, GlobalOrdinal, Node>::constrainRow(Scalar *orig, LocalOrdinal nEntries, Scalar leftBound, Scalar rghtBound,Scalar rsumTarget, Scalar *fixedUnsorted, Scalar *scalarData) const {
/*
   Input
     orig          data that should be adjusted to satisfy bound constraints and
                   row sum constraint. orig is not modified by this function.

     nEntries      length or 'orig'

     leftBound,    define bound constraints for the results.
     rghtBound

     rsumTarget    defines an equality constraint for the row sum

     fixedUnsorted  on output, if a feasible solutuion exists then
                    || orig - fixedUnsorted || = min  when also
                    leftBound <= fixedUnsorted[i] <= rghtBound for all i 
                    and  sum(fixedUnsorted) = rsumTarget.

                    Note: it is possible to use the same pointer for 
                    fixedUnsorted and orig. In this case, orig gets
                    overwritten with the new constraint satisfying values.
 
      scalarData    a work array that should be 3x nEntries.

   On return constrain() indicates whether or not a feasible solution exists.
*/

/*
  Given a sequence of numbers    o1  ... on, fix these so that they are as 
  close as possible to the original but satisfy bound constraints and also 
  have the same row sum as the oi's. If we know who is going to lie on a  
  bound, then the "best" answer (i.e., || o - f ||_2 = min)  perturbs 
  each element that doesn't lie on a bound by the same amount. 
   
  We can represent the oi's by considering scattered points on a number line

                 |                                                   |
                 |                                                   |
   o      o    o |  o        o     o           o       o     o       |o       o 
                 |                                                   |
                                                                      
                 \____/                                              \____/
                 <----                                               <----
                 delta                                               delta

  Bounds are shown by vertical lines. The fi's must lie within the bounds, so 
  the 3 leftmost points must be shifted to the right and the 2 rightmost must
  be shifted to the left. If these fi points are all shifted to the bounds
  while the others remain in the same location, the row sum constraint is 
  likely not satisfied and so more shifting is necessary.  In the figure, the f
  rowsum is too large and so there must be more shifting to the left. 

  To minimize || o - f ||_2, we basically shift all "interiors" by the same 
  amount, denoted delta. The only trick is that some points near bounds are
  still affected by the bounds and so these points might be shifted more or less
  than delta. In the example,t he 3 rightmost points are shifted in the opposite
  direction as delta to the bound. The 4th point is shifted by something less 
  than delta so it does not violate the lower bound. The rightmost point is 
  shifted to the bound by some amount larger than delta. However, the 2nd point
  is shifted by delta (i.e., it lies inside the two bounds). 

  If we know delta, we can figure out everything. If we know which points
  are special (not shifted by delta), we can also figure out everything.
  The problem is these two things (delta and the special points) are 
  inter-connected. An algorithm for computing follows.

  1) move exterior points to the bounds and compute how much the row sum is off
     (rowSumDeviation). We assume now that the new row sum is high, so interior
     points must be shifted left. 

  2) Mark closest point just left of the leftmost bound, closestToLeftBound,
     and compute its distance to the leftmost bound.  Mark closest point to the
     left of the rightmost bound, closestToRghtBound, and compute its distance to
     right bound. There are two cases to consider. 

  3) Case 1: closestToLeftBound is closer than closestToRghtBound.
     Assume that shifting by delta does not move closestToLeftBound past the 
     left bound. This means that it will be shifted by delta. However, 
     closestToRghtBound will be shifted by more than delta.  So the total
     number of points shifted by delta (|interiorToBounds|) includes 
     closestToLeftBound up to and including the point just to the left of 
     closestToRghtBound. So 

                  delta = rowSumDeviation/ |interiorToBounds| .

     Recall that rowSumDeviation already accounts for the non-delta shift of 
     of closestToRightBound.  Now check whether our assumption is valid.

     If delta <= closestToLeftBoundDist, assumption is true so delta can be 
     applied to interiorToBounds ... and we are done.
     Else assumption is false. Shift closestToLeftBound to the left bound. 
     Update rowSumDeviation, interiorToBounds, and identify new 
     closestToLeftBound. Repeat step 3).

     Case 2: closestToRghtBound is closer than closestToLeftBound.
     Assume that shifting by delta does not move closestToRghtBound past right 
     bound. This means that it must be shifted by more than delta to right
     bound.  So the total number of points shifted by delta again includes
     closestToLeftBound up to and including the point just to the left of 
     closestToRghtBound. So again compute

                  delta = rowSumDeviation/ |interiorToBounds| .

     If delta <= closestToRghtBoundDist, assumption is true so delta is
     can be applied to interiorToBounds ... and we are done
     Else  assumption is false. Put closestToRghtBound in the
     interiorToBounds set. Remove it's contribution to rowSumDeviation,
     identify new closestToRghtBound. Repeat step 3)


  To implement, sort the oi's so things like closestToLeftBound is just index
  into sorted array.  Updaing closestToLeftBound or closestToRghtBound requires 
  increment by 1.  |interiorToBounds|= closestToRghtBound -  closestToLeftBound 
  To handle the case when the rowsum is low (requiring right interior shifts),
  just flip the signs on data and use the left-shift code (and then flip back
  before exiting function.
*/
  bool   hasFeasibleSol;
  Scalar notFlippedLeftBound, notFlippedRghtBound, aBigNumber, *origSorted;
  Scalar rowSumDeviation, temp, *fixedSorted, delta;
  Scalar closestToLeftBoundDist, closestToRghtBoundDist;
  LocalOrdinal    closestToLeftBound, closestToRghtBound;
  LocalOrdinal    *inds; 
  bool   flipped;

  notFlippedLeftBound = leftBound; 
  notFlippedRghtBound = rghtBound; 

  if ((Teuchos::ScalarTraits<SC>::real(rsumTarget) >= Teuchos::ScalarTraits<SC>::real(leftBound*as<Scalar>(nEntries))) && 
      (Teuchos::ScalarTraits<SC>::real(rsumTarget) <= Teuchos::ScalarTraits<SC>::real(rghtBound*as<Scalar>(nEntries))))
    hasFeasibleSol = true; 
  else {
    hasFeasibleSol=false; 
    return hasFeasibleSol; 
  }
  flipped    = false;
  // compute aBigNumber to handle some corner cases where we need
  // something large so that an if statement will be false
  aBigNumber = Teuchos::ScalarTraits<SC>::zero();
  for (LocalOrdinal i = 0; i < nEntries; i++) {
    if ( Teuchos::ScalarTraits<SC>::magnitude(orig[i]) > Teuchos::ScalarTraits<SC>::magnitude(aBigNumber)) 
      aBigNumber = Teuchos::ScalarTraits<SC>::magnitude(orig[i]);
  }
  aBigNumber = aBigNumber+ (Teuchos::ScalarTraits<SC>::magnitude(leftBound) + Teuchos::ScalarTraits<SC>::magnitude(rghtBound))*as<Scalar>(100.0);

  origSorted  = &scalarData[0];
  fixedSorted = &(scalarData[nEntries]);
  inds        = (LocalOrdinal    *) &(scalarData[2*nEntries]);

  for (LocalOrdinal i = 0; i < nEntries; i++) inds[i] = i; 
  for (LocalOrdinal i = 0; i < nEntries; i++) origSorted[i] = orig[i];  /* orig no longer used */

  // sort so that  orig[inds] is sorted. 
  std::sort(inds, inds+nEntries,
            [origSorted](LocalOrdinal leftIndex, LocalOrdinal rightIndex)
                 { return Teuchos::ScalarTraits<SC>::real(origSorted[leftIndex]) < Teuchos::ScalarTraits<SC>::real(origSorted[rightIndex]);});

  for (LocalOrdinal i = 0; i < nEntries; i++) origSorted[i] = orig[inds[i]];
  // find entry in origSorted just to the right of the leftBound
  closestToLeftBound = 0;
  while ((closestToLeftBound < nEntries) && (Teuchos::ScalarTraits<SC>::real(origSorted[closestToLeftBound]) <= Teuchos::ScalarTraits<SC>::real(leftBound))) closestToLeftBound++;

  // find entry in origSorted just to the right of the rghtBound
  closestToRghtBound = closestToLeftBound;
  while ((closestToRghtBound < nEntries) && (Teuchos::ScalarTraits<SC>::real(origSorted[closestToRghtBound]) <= Teuchos::ScalarTraits<SC>::real(rghtBound))) closestToRghtBound++;

  // compute distance between closestToLeftBound and the left bound and the 
  // distance between closestToRghtBound and the right bound. 

  closestToLeftBoundDist = origSorted[closestToLeftBound] - leftBound;
  if (closestToRghtBound==nEntries) closestToRghtBoundDist= aBigNumber;
  else                              closestToRghtBoundDist= origSorted[closestToRghtBound] - rghtBound;

  // compute how far the rowSum is off from the target row sum taking into account
  // numbers that have been shifted to satisfy bound constraint

  rowSumDeviation = leftBound*as<Scalar>(closestToLeftBound) + as<Scalar>((nEntries-closestToRghtBound))*rghtBound - rsumTarget; 
  for (LocalOrdinal i=closestToLeftBound; i < closestToRghtBound; i++) rowSumDeviation += origSorted[i];

  // the code that follow after this if statement assumes that rowSumDeviation is positive. If this
  // is not the case, flip the signs of everything so that rowSumDeviation is now positive. 
  // Later we will flip the data back to its original form.
  if (Teuchos::ScalarTraits<SC>::real(rowSumDeviation) < Teuchos::ScalarTraits<SC>::real(Teuchos::ScalarTraits<SC>::zero())) {
    flipped = true;
    temp = leftBound; leftBound = -rghtBound; rghtBound = temp; 

    /* flip sign of origSorted and reverse ordering so that the negative version is sorted */

    if ((nEntries%2) == 1) origSorted[(nEntries/2)  ] =  -origSorted[(nEntries/2)  ];
    for (LocalOrdinal i=0; i < nEntries/2; i++) {
      temp=origSorted[i];
      origSorted[i] = -origSorted[nEntries-1-i]; 
      origSorted[nEntries-i-1] = -temp;
    }
  
    /* reverse bounds */

    LocalOrdinal itemp = closestToLeftBound; 
    closestToLeftBound = nEntries-closestToRghtBound;
    closestToRghtBound = nEntries-itemp; 
    closestToLeftBoundDist = origSorted[closestToLeftBound] - leftBound;
    if (closestToRghtBound==nEntries) closestToRghtBoundDist= aBigNumber;
    else                              closestToRghtBoundDist= origSorted[closestToRghtBound] - rghtBound;

    rowSumDeviation = -rowSumDeviation;
  }

  // initial fixedSorted so bounds are satisfied and interiors correspond to origSorted

  for (LocalOrdinal i =                  0; i < closestToLeftBound; i++) fixedSorted[i] = leftBound;
  for (LocalOrdinal i = closestToLeftBound; i < closestToRghtBound; i++) fixedSorted[i] = origSorted[i];
  for (LocalOrdinal i = closestToRghtBound; i < nEntries;           i++) fixedSorted[i] = rghtBound;

  while ((Teuchos::ScalarTraits<SC>::magnitude(rowSumDeviation) > Teuchos::ScalarTraits<SC>::magnitude(as<Scalar>(1.e-10)*rsumTarget))){ // && ( (closestToLeftBound < nEntries ) || (closestToRghtBound < nEntries))) {
   if (closestToRghtBound !=  closestToLeftBound)
        delta = rowSumDeviation/ as<Scalar>(closestToRghtBound -  closestToLeftBound);
   else delta = aBigNumber; 

   if (Teuchos::ScalarTraits<SC>::magnitude(closestToLeftBoundDist) <= Teuchos::ScalarTraits<SC>::magnitude(closestToRghtBoundDist)) {
      if (Teuchos::ScalarTraits<SC>::magnitude(delta) <= Teuchos::ScalarTraits<SC>::magnitude(closestToLeftBoundDist)) {
        rowSumDeviation = Teuchos::ScalarTraits<SC>::zero(); 
        for (LocalOrdinal i = closestToLeftBound; i < closestToRghtBound ; i++) fixedSorted[i] = origSorted[i] - delta;
      }
      else {
        rowSumDeviation = rowSumDeviation - closestToLeftBoundDist; 
        fixedSorted[closestToLeftBound] = leftBound;
        closestToLeftBound++;
        if (closestToLeftBound < nEntries) closestToLeftBoundDist = origSorted[closestToLeftBound] - leftBound;
        else                               closestToLeftBoundDist = aBigNumber;
      }
   }
   else {
      if (Teuchos::ScalarTraits<SC>::magnitude(delta) <= Teuchos::ScalarTraits<SC>::magnitude(closestToRghtBoundDist)) {
        rowSumDeviation = 0; 
        for (LocalOrdinal i = closestToLeftBound; i < closestToRghtBound ; i++) fixedSorted[i] = origSorted[i] - delta;
      }
      else {
        rowSumDeviation = rowSumDeviation + closestToRghtBoundDist;
//        if (closestToRghtBound < nEntries) { 
          fixedSorted[closestToRghtBound] = origSorted[closestToRghtBound];
          closestToRghtBound++;
 //       }
        if (closestToRghtBound >= nEntries) closestToRghtBoundDist = aBigNumber;
        else                                closestToRghtBoundDist = origSorted[closestToRghtBound] - rghtBound;
      }
    }
  }

  if (flipped) {
    /* flip sign of fixedSorted and reverse ordering so that the positve version is sorted */

    if ((nEntries%2) == 1) fixedSorted[(nEntries/2)  ] =  -fixedSorted[(nEntries/2)  ];
    for (LocalOrdinal i=0; i < nEntries/2; i++) {
      temp=fixedSorted[i];
      fixedSorted[i] = -fixedSorted[nEntries-1-i]; 
      fixedSorted[nEntries-i-1] = -temp;
    }
  }
  for (LocalOrdinal i = 0; i < nEntries; i++)  fixedUnsorted[inds[i]] = fixedSorted[i];

  /* check that no constraints are violated */

  bool lowerViolation = false;
  bool upperViolation = false;
  bool sumViolation = false;
  using TST = Teuchos::ScalarTraits<SC>;
  temp = TST::zero();
  for (LocalOrdinal i = 0; i < nEntries; i++)  { 
    if (TST::real(fixedUnsorted[i]) < TST::real(notFlippedLeftBound)) lowerViolation = true;
    if (TST::real(fixedUnsorted[i]) > TST::real(notFlippedRghtBound)) upperViolation = true;
    temp += fixedUnsorted[i];
  }
  SC tol = as<Scalar>(std::max(1.0e-8, as<double>(100*TST::eps())));
  if (TST::magnitude(temp - rsumTarget) > TST::magnitude(tol*rsumTarget)) sumViolation = true;

  TEUCHOS_TEST_FOR_EXCEPTION(lowerViolation, Exceptions::RuntimeError, "MueLu::SaPFactory::constrainRow: feasible solution but computation resulted in a lower bound violation??? ");
  TEUCHOS_TEST_FOR_EXCEPTION(upperViolation, Exceptions::RuntimeError, "MueLu::SaPFactory::constrainRow: feasible solution but computation resulted in an upper bound violation??? ");
  TEUCHOS_TEST_FOR_EXCEPTION(sumViolation,   Exceptions::RuntimeError, "MueLu::SaPFactory::constrainRow: feasible solution but computation resulted in a row sum violation??? ");

  return hasFeasibleSol;
}


} //namespace MueLu

#endif // MUELU_SAPFACTORY_DEF_HPP

//TODO: restrictionMode_ should use the parameter list.